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rtx-nn / rtx-multimodal: cargo fmt reformatting

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Pure formatting changes across rtx-nn (conv_transpose1d, conv/mod, rnn/lstm)
and rtx-multimodal (audio/generation, audio/source_separation): multi-line
braces, trailing commas, import ordering. No logic changes.

Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
This commit is contained in:
osobh
2026-04-27 18:37:07 -07:00
co-authored by Claude Opus 4.7
parent ed4e5e4b85
commit 6d59251c51
8 changed files with 163 additions and 81 deletions
Download Patch File Download Diff File Expand all files Collapse all files
crates/models/rtx-multimodal/src/audio/source_separation/demucs.rs
+26 -7
View File
@@ -113,7 +113,11 @@ impl DemucsModel {
/// For audio longer than `segment_length`, the input is split into overlapping
/// segments, each processed independently, then recombined via overlap-add with
/// a triangular cross-fade window.
pub fn separate(&mut self, waveform: &[f32], channels: usize) -> Result<Vec<StemOutput>, DemucsError> {
pub fn separate(
&mut self,
waveform: &[f32],
channels: usize,
) -> Result<Vec<StemOutput>, DemucsError> {
if waveform.is_empty() {
return Err(DemucsError::EmptyInput);
}
@@ -138,7 +142,8 @@ impl DemucsModel {
// 4. Run inference on each segment
let stem_types = StemType::stems_for(self.config.num_stems);
let mut stem_accumulators: Vec<Vec<f32>> = vec![vec![0.0; total_frames * channels]; self.config.num_stems];
let mut stem_accumulators: Vec<Vec<f32>> =
vec![vec![0.0; total_frames * channels]; self.config.num_stems];
let mut weight_accumulator: Vec<f32> = vec![0.0; total_frames];
for (start_frame, chunk) in &segments {
@@ -208,7 +213,12 @@ impl DemucsModel {
}
/// Segment audio into overlapping chunks.
fn segment(&self, channel_first: &[f32], channels: usize, total_frames: usize) -> Vec<(usize, Vec<f32>)> {
fn segment(
&self,
channel_first: &[f32],
channels: usize,
total_frames: usize,
) -> Vec<(usize, Vec<f32>)> {
let seg_len = self.config.segment_length;
let hop = ((1.0 - self.config.overlap) * seg_len as f32) as usize;
let hop = hop.max(1);
@@ -272,14 +282,19 @@ impl DemucsModel {
let mut inputs = HashMap::new();
inputs.insert("mix".to_string(), &input_tensor);
let outputs = self.session.run(inputs)
let outputs = self
.session
.run(inputs)
.map_err(|e| DemucsError::Inference(e.to_string()))?;
// Extract the output tensor (first output, whatever its name)
let output_tensor = outputs.into_values().next()
let output_tensor = outputs
.into_values()
.next()
.ok_or_else(|| DemucsError::Inference("no output tensor from ONNX model".into()))?;
let output_data = output_tensor.to_vec_f32()
let output_data = output_tensor
.to_vec_f32()
.map_err(|e| DemucsError::Inference(e.to_string()))?;
Ok(output_data)
@@ -292,7 +307,11 @@ fn normalize(samples: &[f32]) -> (Vec<f32>, f32) {
return (vec![], 1.0);
}
let mean_sq: f64 = samples.iter().map(|&s| (s as f64) * (s as f64)).sum::<f64>() / samples.len() as f64;
let mean_sq: f64 = samples
.iter()
.map(|&s| (s as f64) * (s as f64))
.sum::<f64>()
/ samples.len() as f64;
let rms = mean_sq.sqrt() as f32;
let scale = rms.max(1e-8);